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inference working but SLOW

This commit is contained in:
v0xie 2023-10-18 04:16:01 -07:00
parent ec718f76b5
commit 1c6efdbba7
2 changed files with 75 additions and 40 deletions
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71
extensions-builtin/Lora/network_oft.py
View File

@ -12,6 +12,7 @@ class ModuleTypeOFT(network.ModuleType):
# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
class NetworkModuleOFT(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.oft_blocks = weights.w["oft_blocks"]
@ -20,24 +21,29 @@ class NetworkModuleOFT(network.NetworkModule):
self.dim = self.oft_blocks.shape[0]
self.num_blocks = self.dim
#if type(self.alpha) == torch.Tensor:
# self.alpha = self.alpha.detach().numpy()
if "Linear" in self.sd_module.__class__.__name__:
self.out_dim = self.sd_module.out_features
elif "Conv" in self.sd_module.__class__.__name__:
self.out_dim = self.sd_module.out_channels
self.constraint = self.alpha * self.out_dim
self.constraint = self.alpha
#self.constraint = self.alpha * self.out_dim
self.block_size = self.out_dim // self.num_blocks
self.oft_multiplier = self.multiplier()
self.org_module: list[torch.Module] = [self.sd_module]
self.R = self.get_weight()
self.apply_to()
# replace forward method of original linear rather than replacing the module
# self.org_forward = self.sd_module.forward
# self.sd_module.forward = self.forward
def apply_to(self):
self.org_forward = self.org_module[0].forward
self.org_module[0].forward = self.forward
def get_weight(self):
def get_weight(self, multiplier=None):
if not multiplier:
multiplier = self.multiplier()
block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
@ -45,38 +51,31 @@ class NetworkModuleOFT(network.NetworkModule):
I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
block_R_weighted = multiplier * block_R + (1 - multiplier) * I
R = torch.block_diag(*block_R_weighted)
return R
def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
block_Q = oft_blocks - oft_blocks.transpose(1, 2)
norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
R = torch.block_diag(*block_R_weighted)
#R = self.get_weight().to(orig_weight.device, dtype=orig_weight.dtype)
# W = R*W_0
updown = orig_weight + R
output_shape = [R.size(0), orig_weight.size(1)]
R = self.R
if orig_weight.dim() == 4:
weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
else:
weight = torch.einsum("oi, op -> pi", orig_weight, R)
updown = orig_weight @ R
output_shape = [orig_weight.size(0), R.size(1)]
#output_shape = [R.size(0), orig_weight.size(1)]
return self.finalize_updown(updown, orig_weight, output_shape)
# def forward(self, x, y=None):
# x = self.org_forward(x)
# if self.oft_multiplier == 0.0:
# return x
# R = self.get_weight().to(x.device, dtype=x.dtype)
# if x.dim() == 4:
# x = x.permute(0, 2, 3, 1)
# x = torch.matmul(x, R)
# x = x.permute(0, 3, 1, 2)
# else:
# x = torch.matmul(x, R)
# return x
def forward(self, x, y=None):
x = self.org_forward(x)
if self.multiplier() == 0.0:
return x
R = self.get_weight().to(x.device, dtype=x.dtype)
if x.dim() == 4:
x = x.permute(0, 2, 3, 1)
x = torch.matmul(x, R)
x = x.permute(0, 3, 1, 2)
else:
x = torch.matmul(x, R)
return x

42
extensions-builtin/Lora/networks.py
View File

@ -170,6 +170,10 @@ def load_network(name, network_on_disk):
emb_dict[vec_name] = weight
bundle_embeddings[emb_name] = emb_dict
#if key_network_without_network_parts == "oft_unet":
# print(key_network_without_network_parts)
# pass
key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
@ -185,15 +189,39 @@ def load_network(name, network_on_disk):
elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
elif sd_module is None and "oft_unet" in key_network_without_network_parts:
key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
# some SD1 Loras also have correct compvis keys
if sd_module is None:
key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
elif sd_module is None and "oft_unet" in key_network_without_network_parts:
# UNET_TARGET_REPLACE_MODULE_ALL_LINEAR = ["Transformer2DModel"]
# UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
UNET_TARGET_REPLACE_MODULE_ATTN_ONLY = ["CrossAttention"]
# TODO: Change matchedm odules based on whether all linear, conv, etc
key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
#key_no_suffix = key.rsplit("_to_", 1)[0]
## Match all modules of class CrossAttention
#replace_module_list = []
#for module_type in UNET_TARGET_REPLACE_MODULE_ATTN_ONLY:
# replace_module_list += [module for k, module in shared.sd_model.network_layer_mapping.items() if module_type in module.__class__.__name__]
#matched_module = replace_module_list.get(key_no_suffix, None)
#if key.endswith('to_q'):
# sd_module = matched_module.to_q or None
#if key.endswith('to_k'):
# sd_module = matched_module.to_k or None
#if key.endswith('to_v'):
# sd_module = matched_module.to_v or None
#if key.endswith('to_out_0'):
# sd_module = matched_module.to_out[0] or None
#if key.endswith('to_out_1'):
# sd_module = matched_module.to_out[1] or None
if sd_module is None:
keys_failed_to_match[key_network] = key
continue
@ -215,6 +243,14 @@ def load_network(name, network_on_disk):
net.modules[key] = net_module
# replaces forward method of original Linear
# applied_to_count = 0
#for key, created_module in net.modules.items():
# if isinstance(created_module, network_oft.NetworkModuleOFT):
# net_module.apply_to()
#applied_to_count += 1
# print(f'Applied OFT modules: {applied_to_count}')
embeddings = {}
for emb_name, data in bundle_embeddings.items():
embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)